Goal of the paper

Since reviews on BGG are mostly prepared by enthusiasts, but are used to drive buying decisions of the general population, we want to see what it takes to achieve a favorable position in the general ranking. Are some genres more favored (e.g. strategy/war games)? Does complexity matter? Are controversial games more, or less popular?

Importing the libraries

We start with the standard libraries (tidyverse, ggplot2) and add some interesting extensions.

library(tidyverse)
library(ggplot2)
library(ggthemes)
library(ggrepel)
library(cowplot)
library(gganimate)
library(ggridges)
library(viridis)
library(ggtech) # For the airbnb theme
library(extrafont) # To add the font for Airbnb theme
library(GGally) # Grapihical correlogram
library(knitr) # Nice tables-kables
library(kableExtra)

# Download this fonts and install manually
# https://github.com/ricardo-bion/ggtech/blob/master/Circular%20Air-Medium%203.46.45%20PM.ttf
# https://github.com/ricardo-bion/ggtech/blob/master/Circular%20Air-Bold%203.46.45%20PM.ttf

# Import all fonts, takes a few minutes but removes the annoying warnings
# font_import()
# font_import(pattern = 'Circular', prompt=FALSE)
# loadfonts(device = "win")

# Lets set the theme to this nice one from Airbnb
# Colors: c("#FF5A5F", "#FFB400", "#007A87", "#FFAA91", "#7B0051")
theme_set(theme_airbnb_fancy())
pink = "#FF5A5F"
orange = "#FFB400"
blueGreen = "#007A87"
flesh = "#FFAA91"
purple = "#7B0051"
options(scipen=999) #avoiding e10 notation

Importing the data

Some facts about the dataset: - There are 17 000 board games scrapped from Board Game Geek. - All games have > 30 reviews. - 13 000 games are not included in the dataset, because they had less than 30 reviews. - the second dataset consists of 13 000 000 reviews, each with a number and optionally a comment. - Source: https://www.kaggle.com/jvanelteren/boardgamegeek-reviews - Collected in 2019.

reviews <- 'data/bgg-13m-reviews.csv' %>%
  read_csv()

games <- 'data/games_detailed_info.csv' %>%
  read_csv()

Formating the data

Renaming the variables

We need to drop some columns and rename others to make life easier with ggplot.

# Drop the index
reviews <- reviews %>% 
  select(-X1)


# Rename and drop some variables
games <- games %>%
  select(
    "Abstract_Rank" = "Abstract Game Rank", 
    "Rank" = "Board Game Rank",              
    "Childrens_Rank" = "Children's Game Rank",  
    "Family_Rank" = "Family Game Rank",            
    "Party_Rank" = "Party Game Rank",             
    "Strategy_Rank" = "Strategy Game Rank",
    "Thematic_Rank" = "Thematic Rank",               
    "War_Game_Rank" = "War Game Rank",                             
    "Ratings_average" = "average",                      
    "Complexity" = "averageweight",                
    "Geekscore" = "bayesaverage",
    "Category" = "boardgamecategory",
    "Mechanics" = "boardgamemechanic",           
    'Publisher' = "boardgamepublisher",
    'Description' = "description",                 
    "id",        
    "image",                    
    "maxplayers",                  
    "maxplaytime",                 
    "minage",     
    "minplayers",          
    "minplaytime",    
    "numcomments",    
    "numweights",
    "owned",         
    "playingtime",                 
    "name" = "primary",                     
    "Ratings_std_dev" = "stddev",         
    "thumbnail",          
    "trading",                      
    "usersrated",        
    "wanting",
    "wishing", 
    "yearpublished"
  )

Removing outliers

There are some ‘joke’ games, e.g. a war game that takes 1 500 hours to complete (The Campaign for North Africa).

# Lets remove some outliers to make life easier
# Some outliers are ok, eg. for most popular games the "wishing" will be very high
games <- games %>% 
  filter(yearpublished > 1950,
         maxplayers < 20,
         maxplaytime < 600,
         maxplaytime < 600,
         playingtime < 600)

Adding new variables

Binned complexity

For each game, the participants can vote on the compelxity rating (from 1 to 5). These votes are later averaged and presented as a single score. We want to bin this variable, to make plotting easier. We dropped the 5th cattegory, becuase only a few games have a score of > 4.25.

games %>%
  ggplot(aes(x = Complexity)) +
  geom_histogram(bins = 60, color = orange, fill = orange, alpha = 0.5) +
  labs(title = 'Distribution on average complexity', x = 'Complexity', y = 'Count')

# Values are from 1-5, but there is very little after 4.25
games$ComplexityBinned <- games$Complexity %>% 
  cut(
    breaks = c(-Inf, 1.5, 2.5, 3.25, Inf),
    labels = c(1, 2, 3, 4))

a <- games$ComplexityBinned %>% 
  table()

kable(a, col.names = c('Complexity rating', 'Number of games')) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "responsive"), full_width = F)

Complexity rating	Number of games
1	5487
2	6586
3	2992
4	1274

Binary categories

In addition to the main ranking, BGG proviedes additional ratings for some main categories. We want to see if there is some overlaping between this rankings.

# Create binary variables 
# A game can have multiple main categories, so we can't create one column
games$isAbstract <- as.integer( !is.na(games$Abstract_Rank) )
games$isChildrens <- as.integer( !is.na(games$Childrens_Rank) )
games$isFamily <- as.integer( !is.na(games$Family_Rank) )
games$isParty <- as.integer( !is.na(games$Party_Rank) )
games$isStrategy <- as.integer( !is.na(games$Strategy_Rank) )
games$isThematic <- as.integer( !is.na(games$Thematic_Rank) )
games$isWarGame <- as.integer( !is.na(games$War_Game_Rank) )

# Some overlap between categories
a <- games %>%
  group_by(isAbstract,
           isChildrens,
           isFamily,
           isParty,
           isStrategy,
           isThematic,
           isWarGame) %>%
  summarise(n = n()) %>%
  arrange(desc(n))

a[a == 0] <- ''

kable(a, col.names = c('Abstract',
                       'Children',
                       'Family',
                       'Party',
                       'Strategy',
                       'Thematic', 
                       'War game',
                       'Number of games')) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"), full_width = F)

Abstract	Children	Family	Party	Strategy	Thematic	War game	Number of games
							8094
						1	2548
				1			1247
		1					1036
1							719
	1						600
					1		576
		1		1			311
			1				302
				1	1		209
		1	1				117
					1	1	108
		1			1		96
	1	1					88
1		1					87
				1		1	70
1				1			39
			1		1		33
1	1						20
1						1	10
			1	1			6
		1				1	5
	1		1				5
				1	1	1	4
	1					1	4
	1	1	1				1
1					1		1
1				1	1		1
1			1				1
1	1					1	1

There is - this creates a challange. How to choose a main category for each game?

Determining the main category

Creating quantiles for each ranking

For each game we create a quantile rank - 0.01 means that the game is in top 1% in the category.

# Convert category ranks to quantiles
games$quantileAbstract <- games %>%
  .$Abstract_Rank / max(games$Abstract_Rank, na.rm = TRUE)

games$quantileChildrens <- games %>%
  .$Childrens_Rank / max(games$Childrens_Rank, na.rm = TRUE)

games$quantileFamily <- games %>%
  .$Family_Rank / max(games$Family_Rank, na.rm = TRUE)

games$quantileParty <- games %>%
  .$Party_Rank / max(games$Party_Rank, na.rm = TRUE)

games$quantileStrategy <- games %>%
  .$Strategy_Rank / max(games$Strategy_Rank, na.rm = TRUE)

games$quantileThematic <- games %>%
  .$Thematic_Rank / max(games$Thematic_Rank, na.rm = TRUE)

games$quantileWarGame <- games %>%
  .$War_Game_Rank / max(games$War_Game_Rank, na.rm = TRUE)

Finding the main category

To find the main category of the game, we look at all the quantiles and choose the lowest one. Our logic is that it is much more significant if the game is 5th in ranking that has 100 games, than in one that has 1000.

# Find the primary category
# How? By the lowest quantile, eg. the most important in this catergory
find_lowest_quantile <- function(row, output) {
  abstract = row["quantileAbstract"]
  family = row["quantileChildrens"]
  child = row["quantileFamily"]
  party = row["quantileParty"]
  strategy = row["quantileStrategy"]
  thematic = row["quantileThematic"]
  war = row["quantileWarGame"]
  other = 1
  
  lowest <- min(
    abstract,
    family,
    child,
    party,
    strategy,
    thematic,
    war,
    other,
    na.rm = TRUE)
  
  case_when(
    lowest == abstract ~ 'Abstract',
    lowest == family ~ 'Family',
    lowest == child ~ "Children",
    lowest == party ~ 'Party',
    lowest == strategy ~ 'Strategy',
    lowest == thematic ~ 'Thematic',
    lowest == war ~ 'War Game',
    lowest == 1 ~ 'Other'
  ) %>%
    return()
}

# Takes a while
games$cat <- games %>%
  apply(1, find_lowest_quantile) %>%
  as.factor()

# A nice distribution, a lot of other 
a <- games$cat %>% table()
kable(a)

.	Freq
Abstract	852
Children	1453
Family	718
Other	8098
Party	445
Strategy	1249
Thematic	791
War Game	2733

my_dens <- function(data, mapping, ...) {
  ggplot(data = data, mapping = mapping) +
    geom_density_ridges(aes(y = games$ComplexityBinned, fill = games$ComplexityBinned), scale = 3, rel_min_height = 0.01, alpha = 0.7, color = NA ) + 
    scale_fill_manual(values = c("#FF5A5F", "#FFB400", "#007A87", "#FFAA91", "#7B0051")) 
}

my_dots <- function(data, mapping, ...) {
  ggplot(data = data, mapping = mapping) +
    geom_point(alpha = 0.1) + 
    scale_color_tech(theme="airbnb") 
}

games %>%
  select( "Rank",
          "Ratings_average",
          "yearpublished",
          "usersrated",
          "playingtime",   
          "usersrated") %>%
  ggpairs(
    mapping = aes(color = games$ComplexityBinned, fill = games$ComplexityBinned),
    diag = list(continuous = my_dens),
    lower = list(continuous = my_dots),
    progress = TRUE,
    axisLabels = 'none',
    legend = 1) + 
  theme(text = element_text(size = 10)) +
  labs(title = '')

# 002 - Boardgames per year
games %>%
  ggplot(aes(x = yearpublished)) +
  geom_density(color = orange, fill = orange, alpha = 0.5) +
  labs(title = 'Board games published per year', x = 'Year', y = 'Density')

# 003 - Average ratings vs year (>2009)
plot <- games %>% 
  filter(yearpublished > 2009) %>%
  ggplot(aes(x = Ratings_average)) +
  geom_density(color = orange, fill = orange, alpha = 0.5) +  
  labs(title = 'Ratings of games published in: {floor(frame_time)}', x = 'Ratings', y = 'Density') +
  transition_time(yearpublished) +
  ease_aes('linear') 

plot %>% animate(width = 700, height = 300)

games %>%
  ggplot(aes(x = Ratings_average, y = ComplexityBinned, fill = ComplexityBinned)) +
  geom_density_ridges(scale = 3, rel_min_height = 0.01, alpha = 0.7, color = NA) + 
  scale_fill_manual(values = c("#FF5A5F", "#FFB400", "#007A87", "#FFAA91", "#7B0051")) +
  scale_x_continuous(limits = c(3, 9.3)) +
  labs(title = 'Does the complexity of the game infuence the ratings?',
       x = 'Ratings',
       y = 'Complexity of the game',
       fill = 'Complexity')

games %>% 
  ggplot(aes(x = Ratings_average, y = Rank)) +
  geom_point(aes(colour = Rank), alpha = 0.2) +
  scale_color_gradient(low = orange, high = purple) +
  labs(title = 'Does the average rating influence the final rank?',
       subtitle = 'Chicken Drumstick distribution',
       x = 'Average rating',
       y = 'Overall rank') +
  theme(legend.position = 'none') +
  scale_y_reverse()

games %>% 
  ggplot(aes(x = log10(usersrated), y = Rank)) +
  geom_point(aes(colour = Rank), alpha = 0.3) + 
  scale_color_gradient(low = orange, high = purple) +
  labs(title = 'Does the number of user ratings influence the final rank?',
       subtitle = 'Jaws distribution',
       x = 'Log 10 of the number of ratings ',
       y = 'Overall rank') +
  theme(legend.position = 'none') +
  scale_y_reverse()

# 007 rank vs category
games$isTop <- ifelse(games$Rank < 300, 1, 0)

games %>%
  ggplot(aes(x = Rank, fill = cat)) +
  geom_density(alpha = 0.7, color = NA) + 
  facet_wrap('cat') + 
  scale_fill_manual(values = c("#FF5A5F", "#FFB400", "#007A87", "#FFAA91", "#7B0051", "#FF5A5F", "#FFB400", "#007A87")) +
  theme(legend.position = 'none') +
  labs(title = 'Does the main category of the game infuence the rank?',
       subtitle = 'All games',
       x = 'Rank (lower is better)',
       y = 'Density')

games %>%
  filter(isTop == 1) %>%
  ggplot(aes(x = Rank, fill = cat)) +
  geom_histogram(alpha = 0.7, color = NA, bins = 30) + 
  facet_wrap('cat') + 
  scale_fill_manual(values = c("#FF5A5F", "#FFB400", "#007A87", "#FFAA91", "#7B0051", "#FF5A5F", "#FFB400", "#007A87")) +
  theme(legend.position = 'none') +
  labs(title = 'Does the main category of the game infuence the rank?',
       subtitle = 'Top 300 games',
       x = 'Rank (lower is better)',
       y = 'Count')

# See the top 1% of abstract games, and their rank
a <- games %>%
  filter(quantileAbstract < 0.01 ) %>%
  {paste0(.$name, ' ' , .$Rank)}
kable(a)

x
Patchwork 64
Azul 37
Santorini 101
Sagrada 125
Onitama 205
YINSH 157
DVONN 386
TZAAR 357

# 008 Wishing vs rank
games %>%
  filter(wishing > 10) %>%
  ggplot(aes(x = log10(wishing), y = Rank)) +
  geom_point(aes(colour = Rank), alpha = 0.3) + 
  geom_smooth(color = purple) + 
  scale_color_gradient(low = orange, high = purple) +
  labs(title = 'Does the number of user wishlisting influence the final rank?',
       subtitle = 'Removed obeservations with < 10 users',
       x = 'Log 10 of the number of users wishlisting',
       y = 'Overall rank') +
  theme(legend.position = 'none') +
  scale_y_reverse()

# 009 - what does not influence 
# % of written comments
games %>%
  filter(numcomments / usersrated < 1) %>% 
  ggplot(aes(x = numcomments / usersrated , y = Rank)) +
  geom_point(aes(colour = Rank), alpha = 0.5) + 
  scale_color_gradient(low = orange, high = purple) +
  labs(title = '% of ratings with written reviews',
       x = 'Number of comments / Number of ratings',
       y = 'Overall rank') +
  theme(legend.position = 'none') +
  scale_y_reverse()

# Std dev 
games %>%
  ggplot(aes(x = Ratings_std_dev, y = Rank))+
  geom_point(aes(colour = Rank), alpha = 0.5) + 
  scale_color_gradient(low = orange, high = purple) +
  labs(title = 'How controversial the game is',
       x = 'Standard deviation of ratings',
       y = 'Overall rank') +
  theme(legend.position = 'none') +
  scale_y_reverse()

games %>%
  filter(Ratings_std_dev > 0.75,
         Ratings_std_dev < 2.5) %>%
  ggplot(aes(x = Ratings_std_dev, y = Rank)) +
  stat_density_2d(aes(fill = ..density..), geom = "raster", contour = FALSE) +
  scale_x_continuous(expand = c(0, 0)) +
  scale_y_continuous(expand = c(0, 0)) + 
  scale_fill_gradient(high = orange, low = purple)

Board Game Extravaganza

Kornel Krysiak & Michał Szałański

Background